System and method for data compaction utilizing mismatch probability estimation

ABSTRACT

A system and method for compacting data that uses mismatch probability estimation to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A “mismatch codeword” is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook. During encoding, if a mismatch occurs, a secondary encoding process is used to encode the mismatched sourceblock.

CROSS-REFERENCE TO RELATED APPLICATIONS

Priority is claimed in the application data sheet to the followingpatents or patent applications, each of which is expressly incorporatedherein by reference in its entirety:

-   -   Ser. No. 17/974,230    -   Ser. No. 17/884,470    -   63/232,050    -   Ser. No. 17/727,913    -   Ser. No. 17/404,699    -   Ser. No. 16/455,655    -   Ser. No. 16/200,466    -   Ser. No. 15/975,741    -   62/578,824

BACKGROUND OF THE INVENTION Field of the Invention

The present invention is in the field of computer data encoding, and inparticular the usage of encoding for enhanced security and compaction ofdata.

Discussion of the State of the Art

As computers become an ever-greater part of our lives, and especially inthe past few years, data storage has become a limiting factor worldwide.Prior to about 2010, the growth of data storage far exceeded the growthin storage demand. In fact, it was commonly considered at that time thatstorage was not an issue, and perhaps never would be, again. In 2010,however, with the growth of social media, cloud data centers, high techand biotech industries, global digital data storage acceleratedexponentially, and demand hit the zettabyte (1 trillion gigabytes)level. Current estimates are that data storage demand will reach 175zettabytes by 2025. By contrast, digital storage device manufacturersproduced roughly 1 zettabyte of physical storage capacity globally in2016. We are producing data at a much faster rate than we are producingthe capacity to store it. In short, we are running out of room to storedata, and need a breakthrough in data storage technology to keep up withdemand.

The primary solutions available at the moment are the addition ofadditional physical storage capacity and data compression. As notedabove, the addition of physical storage will not solve the problem, asstorage demand has already outstripped global manufacturing capacity.Data compression is also not a solution. A rough average compressionratio for mixed data types is 2:1, representing a doubling of storagecapacity. However, as the mix of global data storage trends towardmulti-media data (audio, video, and images), the space savings yieldedby compression either decreases substantially, as is the case withlossless compression which allows for retention of all original data inthe set, or results in degradation of data, as is the case with lossycompression which selectively discards data in order to increasecompression. Even assuming a doubling of storage capacity, datacompression cannot solve the global data storage problem.

Transmission bandwidth is also increasingly becoming a bottleneck. Largedata sets require tremendous bandwidth, and we are transmitting more andmore data every year between large data centers. On the small end of thescale, we are adding billions of low bandwidth devices to the globalnetwork, and data transmission limitations impose constraints on thedevelopment of networked computing applications such as the “Internet ofThings.”

Furthermore, as quantum computing becomes more and more imminent, thesecurity of data, both stored data and data streaming from one point toanother via networks, becomes a critical concern as existing encryptiontechnologies based on difficult-to-solve mathematical calculations areplaced at risk.

Entropy encoding methods can be used to partially solve some of thesedata compaction issues. However, existing entropy encoding methodseither fail to account for, or inefficiently encode, data that has notpreviously been processed by the encoding method, and thus lead toinefficient compaction of data in many cases.

What is needed is a system and method for compacting data that usesmismatch probability estimation to improve entropy encoding methods toaccount for, and efficiently handle, previously-unseen data in data tobe compacted.

SUMMARY OF THE INVENTION

The inventor has developed a system and method for a system and methodfor compacting data that uses mismatch probability estimation to improveentropy encoding methods to account for, and efficiently handle,previously-unseen data in data to be compacted. Training data sets areanalyzed to determine the frequency of occurrence of each sourceblock inthe training data sets. A mismatch probability estimate is calculatedcomprising an estimated frequency at which any given data sourceblockreceived during encoding will not have a codeword in the codebook.Entropy encoding is used to generate codebooks comprising codewords fordata sourceblocks based on the frequency of occurrence of eachsourceblock. A “mismatch codeword” is inserted into the codebook basedon the mismatch probability estimate to represent those cases when ablock of data to be encoded does not have a codeword in the codebook.During encoding, if a mismatch occurs, a secondary encoding process isused to encode the mismatched sourceblock.

According to a preferred embodiment, a system for encoding data usingmismatch probability estimation is disclosed, comprising: a computingdevice comprising a processor, a memory, and a non-volatile data storagedevice; a statistical analyzer comprising a first plurality ofprogramming instructions stored in the memory which, when operating onthe processor, causes the computing device to: receive a training dataset for encoding, the training data set comprising sourceblocks of data;determine a frequency of occurrence of each sourceblock of the trainingdata set; calculate a mismatch probability estimate comprising aprobability that any given sourceblock in a non-training data set to belater received for encoding will not be a sourceblock that was containedin the training data set; generate a mismatch sourceblock representingsourceblocks that were not contained in the training data set, andassign the mismatch probability estimate to the mismatch sourceblock asthe frequency of occurrence of the mismatch sourceblock; and a codebookgenerator comprising a second plurality of programming instructionsstored in the memory which, when operating on the processor, causes thecomputing device to: generate a codebook from the sourceblocks of thetraining data set and the mismatch sourceblock using an entropy encodingmethod wherein codewords are assigned to each sourceblock based on itsfrequency of occurrence.

According to another preferred embodiment, a method for encoding datausing mismatch probability estimation is disclosed, comprising the stepsof: using a statistical analyzer operating on a computing devicecomprising a memory and a processor to: receive a training data set forencoding, the training data set comprising sourceblocks of data;determine a frequency of occurrence of each sourceblock of the trainingdata set; calculate a mismatch probability estimate comprising aprobability that any given sourceblock in a non-training data set to belater received for encoding will not be a sourceblock that was containedin the training data set; generate a mismatch sourceblock representingsourceblocks that were not contained in the training data set, andassign the mismatch probability estimate to the mismatch sourceblock asthe frequency of occurrence of the mismatch sourceblock; and using acodebook generator operating on the computing device to: generate acodebook from the sourceblocks of the training data set and the mismatchsourceblock using an entropy encoding method wherein codewords areassigned to each sourceblock based on its frequency of occurrence

According to an aspect of an embodiment, an encoder operating on thecomputing device is used to: receive the non-training data set forencoding, the non-training data set comprising sourceblocks of data; foreach sourceblock of the non-training data set, look up and return thecodeword for that sourceblock in the codebook and insert that codewordinto an encoded data stream; where the returned codeword is the codewordfor the mismatch sourceblock, generate a new codeword for the looked upsourceblock using a secondary encoding method, and insert the newcodeword into the encoded data stream.

According to an aspect of an embodiment, a decoder operating on thecomputing device is used to: receive an encoded data stream comprisingcodewords; for each codeword in the encoded data stream, look up andreturn the sourceblock for that codeword in the codebook and insert thatsourceblock into a decoded data stream; and where the returnedsourceblock is the mismatch sourceblock, determine the sourceblock forthat codeword using the secondary encoding method, and insert thedetermined sourceblock into the decoded data stream.

According to an aspect of an embodiment, the training data set is alow-entropy data set, either having a small subset of sourceblocks of agiven size relative to the total possible number of sourceblocks of thatsize or having a set of sourceblocks closely matching the set ofsourceblocks expected in the non-training data set.

According to an aspect of an embodiment, the entropy encoding method isHuffman coding or a known variant thereof.

According to an aspect of an embodiment, the mismatch probabilityestimate, q, is calculated as q=M/N, where: M is the number of times apreviously-unobserved sourceblock appeared in the training data set; andN is the total number of sourceblocks observed in the training data set.

According to an aspect of an embodiment, the mismatch probabilityestimate, q, is calculated as qM/N=(Σ_(j=1) ^(N)X_(j))/N, where:

$X_{j} = \left\{ {\begin{matrix}{{1{if}S_{j}} \notin \left\{ {{S_{i}\text{:}1} \leq i < j} \right\}} \\{0{otherwise}}\end{matrix};} \right.$

and N is the total number of sourceblocks observed in the training dataset.

According to an aspect of an embodiment, an exponentially-weightedmoving average is applied to the calculation of q=(Σ_(j=1) ^(N)X_(j))/N.

According to an aspect of an embodiment, the exponentially-weightedmoving average is a modified form of an exponentially-weighted movingaverage of the form: μ_(j)=

$\left\{ {\begin{matrix}{{1{if}j} = 0} \\{{{\left( {1 - \beta_{j}} \right)\mu_{j - 1}} + {\beta_{j}X_{j}{if}j}} > 0}\end{matrix};} \right.$

where β_(j)=C log(j)/j and β₁=1, for some constant C.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a diagram showing an embodiment of the system in which allcomponents of the system are operated locally.

FIG. 2 is a diagram showing an embodiment of one aspect of the system,the data deconstruction engine.

FIG. 3 is a diagram showing an embodiment of one aspect of the system,the data reconstruction engine.

FIG. 4 is a diagram showing an embodiment of one aspect of the system,the library management module.

FIG. 5 is a diagram showing another embodiment of the system in whichdata is transferred between remote locations.

FIG. 6 is a diagram showing an embodiment in which a standardizedversion of the sourceblock library and associated algorithms would beencoded as firmware on a dedicated processing chip included as part ofthe hardware of a plurality of devices.

FIG. 7 is a diagram showing an example of how data might be convertedinto reference codes using an aspect of an embodiment.

FIG. 8 is a method diagram showing the steps involved in using anembodiment to store data.

FIG. 9 is a method diagram showing the steps involved in using anembodiment to retrieve data.

FIG. 10 is a method diagram showing the steps involved in using anembodiment to encode data.

FIG. 11 is a method diagram showing the steps involved in using anembodiment to decode data.

FIG. 12 is a diagram showing an exemplary system architecture, accordingto a preferred embodiment of the invention.

FIG. 13 is a diagram showing a more detailed architecture for acustomized library generator.

FIG. 14 is a diagram showing a more detailed architecture for a libraryoptimizer.

FIG. 15 is a diagram showing a more detailed architecture for atransmission and storage engine.

FIG. 16 is a method diagram illustrating key system functionalityutilizing an encoder and decoder pair.

FIG. 17 is a method diagram illustrating possible use of a hybridencoder/decoder to improve the compression ratio.

FIG. 18 is a flow diagram illustrating the use of a data encoding systemused to recursively encode data to further reduce data size.

FIG. 19 is an exemplary system architecture of a data encoding systemused for cyber security purposes.

FIG. 20 is a flow diagram of an exemplary method used to detectanomalies in received encoded data and producing a warning.

FIG. 21 is a flow diagram of a data encoding system used for DistributedDenial of Service (DDoS) attack denial.

FIG. 22 is an exemplary system architecture of a data encoding systemused for data mining and analysis purposes.

FIG. 23 is a flow diagram of an exemplary method used to enablehigh-speed data mining of repetitive data.

FIG. 24 is an exemplary system architecture of a data encoding systemused for remote software and firmware updates.

FIG. 25 is a flow diagram of an exemplary method used to encode andtransfer software and firmware updates to a device for installation, forthe purposes of reduced bandwidth consumption.

FIG. 26 is an exemplary system architecture of a data encoding systemused for large-scale software installation such as operating systems.

FIG. 27 is a flow diagram of an exemplary method used to encode newsoftware and operating system installations for reduced bandwidthrequired for transference.

FIG. 28 is a block diagram of an exemplary system architecture of acodebook training system for a data encoding system, according to anembodiment.

FIG. 29 is a block diagram of an exemplary architecture for a codebooktraining module, according to an embodiment.

FIG. 30 is a block diagram of another embodiment of the codebooktraining system using a distributed architecture and a modified trainingmodule.

FIG. 31 is a method diagram illustrating the steps involved in using anembodiment of the codebook training system to update a codebook.

FIG. 32 is an exemplary system architecture for an encoding system withmultiple codebooks.

FIG. 33 is a flow diagram describing an exemplary algorithm for encodingof data using multiple codebooks.

FIG. 34 is a flow diagram describing an exemplary codebook sortingalgorithm for determining a plurality of codebooks to be shuffledbetween during the encoding process.

FIG. 35 is a diagram showing an exemplary codebook shuffling method.

FIG. 36 is a block diagram of an exemplary system architecture forencoding data using mismatch probability estimates, according to anembodiment.

FIG. 37 is a diagram illustrating an exemplary method for codebookgeneration from a training data set.

FIG. 38 is a diagram illustrating an exemplary encoding using a codebookand secondary encoding.

FIG. 39 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 40 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 41 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 42 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION OF THE DRAWING FIGURES

The inventor has conceived, and reduced to practice, a system and methodfor compacting data that uses mismatch probability estimation to improveentropy encoding methods to account for, and efficiently handle,previously-unseen data in data to be compacted. Training data sets areanalyzed to determine the frequency of occurrence of each sourceblock inthe training data sets. A mismatch probability estimate is calculatedcomprising an estimated frequency at which any given data sourceblockreceived during encoding will not have a codeword in the codebook.Entropy encoding is used to generate codebooks comprising codewords fordata sourceblocks based on the frequency of occurrence of eachsourceblock. A “mismatch codeword” is inserted into the codebook basedon the mismatch probability estimate to represent those cases when ablock of data to be encoded does not have a codeword in the codebook.During encoding, if a mismatch occurs, a secondary encoding process isused to encode the mismatched sourceblock.

Entropy encoding methods (also known as entropy coding methods) arelossless data compression methods which replace fixed-length data inputswith variable-length prefix-free codewords based on the frequency oftheir occurrence within a given distribution. This reduces the number ofbits required to store the data inputs, limited by the entropy of thetotal data set. The most well-known entropy encoding method is Huffmancoding, which will be used in the examples herein.

Because any lossless data compression method must have a code lengthsufficient to account for the entropy of the data set, entropy encodingis most compact where the entropy of the data set is small. However,smaller entropy in a data set means that, by definition, the data setcontains fewer variations of the data. So, the smaller the entropy of adata set used to create a codebook using an entropy encoding method, thelarger is the probability that some piece of data to be encoded will notbe found in that codebook. Adding new data to the codebook leads toinefficiencies that undermine the use of a low-entropy data set tocreate the codebook.

This disadvantage of entropy encoding methods can be overcome bymismatch probability estimation, wherein the probability of encounteringdata that is not in the codebook is calculated in advance, and a special“mismatch codework” is incorporated into the codebook (the primaryencoding algorithm) to represent the expected frequency of encounteringpreviously-unencountered data. When previously-unencountered data isencountered during encoding, attempting to encode thepreviously-unencountered data results in the mismatch codeword, whichtriggers a secondary encoding algorithm to encode thatpreviously-unencountered data. The secondary encoding algorithm mayresult in a less-than-optimal encoding of the previously-unencountereddata, but the efficiencies of using a low-entropy primary encoding makeup for the inefficiencies of the secondary encoding algorithm. Becausethe use of the secondary encoding algorithm has been accounted for inthe primary encoding algorithm by the mismatch probability estimation,the overall efficiency of compaction is improved over other entropyencoding methods.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

The term “bit” refers to the smallest unit of information that can bestored or transmitted. It is in the form of a binary digit (either 0 or1). In terms of hardware, the bit is represented as an electrical signalthat is either off (representing 0) or on (representing 1).

The term “byte” refers to a series of bits exactly eight bits in length.

The term “codebook” refers to a database containing sourceblocks eachwith a pattern of bits and reference code unique within that library.The terms “library” and “encoding/decoding library” are synonymous withthe term codebook.

The terms “compression” and “deflation” as used herein mean therepresentation of data in a more compact form than the original dataset.Compression and/or deflation may be either “lossless,” in which the datacan be reconstructed in its original form without any loss of theoriginal data, or “lossy” in which the data can be reconstructed in itsoriginal form, but with some loss of the original data.

The terms “compression factor” and “deflation factor” as used hereinmean the net reduction in size of the compressed data relative to theoriginal data (e.g., if the new data is 70% of the size of the original,then the deflation/compression factor is 30% or 0.3.)

The terms “compression ratio” and “deflation ratio,” and as used hereinall mean the size of the original data relative to the size of thecompressed data (e.g., if the new data is 70% of the size of theoriginal, then the deflation/compression ratio is 70% or 0.7.)

The term “data” means information in any computer-readable form.

The term “data set” refers to a grouping of data for a particularpurpose. One example of a data set might be a word processing filecontaining text and formatting information.

The term “effective compression” or “effective compression ratio” refersto the additional amount data that can be stored using the method hereindescribed versus conventional data storage methods. Although the methodherein described is not data compression, per se, expressing theadditional capacity in terms of compression is a useful comparison.

The term “sourcepacket” as used herein means a packet of data receivedfor encoding or decoding. A sourcepacket may be a portion of a data set.

The term “sourceblock” as used herein means a defined number of bits orbytes used as the block size for encoding or decoding. A sourcepacketmay be divisible into a number of sourceblocks. As one non-limitingexample, a 1 megabyte sourcepacket of data may be encoded using 512 bytesourceblocks. The number of bits in a sourceblock may be dynamicallyoptimized by the system during operation. In one aspect, a sourceblockmay be of the same length as the block size used by a particular filesystem, typically 512 bytes or 4,096 bytes.

The term “codeword” refers to the reference code form in which data isstored or transmitted in an aspect of the system. A codeword consists ofa reference code or “codeword” to a sourceblock in the library plus anindication of that sourceblock's location in a particular data set.

Conceptual Architecture

FIG. 1 is a diagram showing an embodiment 100 of the system in which allcomponents of the system are operated locally. As incoming data 101 isreceived by data deconstruction engine 102. Data deconstruction engine102 breaks the incoming data into sourceblocks, which are then sent tolibrary manager 103. Using the information contained in sourceblocklibrary lookup table 104 and sourceblock library storage 105, librarymanager 103 returns reference codes to data deconstruction engine 102for processing into codewords, which are stored in codeword storage 106.When a data retrieval request 107 is received, data reconstructionengine 108 obtains the codewords associated with the data from codewordstorage 106, and sends them to library manager 103. Library manager 103returns the appropriate sourceblocks to data reconstruction engine 108,which assembles them into the proper order and sends out the data in itsoriginal form 109.

FIG. 2 is a diagram showing an embodiment of one aspect 200 of thesystem, specifically data deconstruction engine 201. Incoming data 202is received by data analyzer 203, which optimally analyzes the databased on machine learning algorithms and input 204 from a sourceblocksize optimizer, which is disclosed below. Data analyzer may optionallyhave access to a sourceblock cache 205 of recently-processedsourceblocks, which can increase the speed of the system by avoidingprocessing in library manager 103. Based on information from dataanalyzer 203, the data is broken into sourceblocks by sourceblockcreator 206, which sends sourceblocks 207 to library manager 203 foradditional processing. Data deconstruction engine 201 receives referencecodes 208 from library manager 103, corresponding to the sourceblocks inthe library that match the sourceblocks sent by sourceblock creator 206,and codeword creator 209 processes the reference codes into codewordscomprising a reference code to a sourceblock and a location of thatsourceblock within the data set. The original data may be discarded, andthe codewords representing the data are sent out to storage 210.

FIG. 3 is a diagram showing an embodiment of another aspect of system300, specifically data reconstruction engine 301. When a data retrievalrequest 302 is received by data request receiver 303 (in the form of aplurality of codewords corresponding to a desired final data set), itpasses the information to data retriever 304, which obtains therequested data 305 from storage. Data retriever 304 sends, for eachcodeword received, a reference codes from the codeword 306 to librarymanager 103 for retrieval of the specific sourceblock associated withthe reference code. Data assembler 308 receives the sourceblock 307 fromlibrary manager 103 and, after receiving a plurality of sourceblockscorresponding to a plurality of codewords, assembles them into theproper order based on the location information contained in eachcodeword (recall each codeword comprises a sourceblock reference codeand a location identifier that specifies where in the resulting data setthe specific sourceblock should be restored to. The requested data isthen sent to user 309 in its original form.

FIG. 4 is a diagram showing an embodiment of another aspect of thesystem 400, specifically library manager 401. One function of librarymanager 401 is to generate reference codes from sourceblocks receivedfrom data deconstruction engine 301. As sourceblocks are received 402from data deconstruction engine 301, sourceblock lookup engine 403checks sourceblock library lookup table 404 to determine whether thosesourceblocks already exist in sourceblock library storage 105. If aparticular sourceblock exists in sourceblock library storage 105,reference code return engine 405 sends the appropriate reference code406 to data deconstruction engine 301. If the sourceblock does not existin sourceblock library storage 105, optimized reference code generator407 generates a new, optimized reference code based on machine learningalgorithms. Optimized reference code generator 407 then saves thereference code 408 to sourceblock library lookup table 104; saves theassociated sourceblock 409 to sourceblock library storage 105; andpasses the reference code to reference code return engine 405 forsending 406 to data deconstruction engine 301. Another function oflibrary manager 401 is to optimize the size of sourceblocks in thesystem. Based on information 411 contained in sourceblock library lookuptable 104, sourceblock size optimizer 410 dynamically adjusts the sizeof sourceblocks in the system based on machine learning algorithms andoutputs that information 412 to data analyzer 203. Another function oflibrary manager 401 is to return sourceblocks associated with referencecodes received from data reconstruction engine 301. As reference codesare received 414 from data reconstruction engine 301, reference codelookup engine 413 checks sourceblock library lookup table 415 toidentify the associated sourceblocks; passes that information tosourceblock retriever 416, which obtains the sourceblocks 417 fromsourceblock library storage 105; and passes them 418 to datareconstruction engine 301.

FIG. 5 is a diagram showing another embodiment of system 500, in whichdata is transferred between remote locations. As incoming data 501 isreceived by data deconstruction engine 502 at Location 1, datadeconstruction engine 301 breaks the incoming data into sourceblocks,which are then sent to library manager 503 at Location 1. Using theinformation contained in sourceblock library lookup table 504 atLocation 1 and sourceblock library storage 505 at Location 1, librarymanager 503 returns reference codes to data deconstruction engine 301for processing into codewords, which are transmitted 506 to datareconstruction engine 507 at Location 2. In the case where the referencecodes contained in a particular codeword have been newly generated bylibrary manager 503 at Location 1, the codeword is transmitted alongwith a copy of the associated sourceblock. As data reconstruction engine507 at Location 2 receives the codewords, it passes them to librarymanager module 508 at Location 2, which looks up the sourceblock insourceblock library lookup table 509 at Location 2 and retrieves theassociated from sourceblock library storage 510. Where a sourceblock hasbeen transmitted along with a codeword, the sourceblock is stored insourceblock library storage 510 and sourceblock library lookup table 504is updated. Library manager 503 returns the appropriate sourceblocks todata reconstruction engine 507, which assembles them into the properorder and sends the data in its original form 511.

FIG. 6 is a diagram showing an embodiment 600 in which a standardizedversion of a sourceblock library 603 and associated algorithms 604 wouldbe encoded as firmware 602 on a dedicated processing chip 601 includedas part of the hardware of a plurality of devices 600. Contained ondedicated chip 601 would be a firmware area 602, on which would bestored a copy of a standardized sourceblock library 603 anddeconstruction/reconstruction algorithms 604 for processing the data.Processor 605 would have both inputs 606 and outputs 607 to otherhardware on the device 600. Processor 605 would store incoming data forprocessing on on-chip memory 608, process the data using standardizedsourceblock library 603 and deconstruction/reconstruction algorithms604, and send the processed data to other hardware on device 600. Usingthis embodiment, the encoding and decoding of data would be handled bydedicated chip 601, keeping the burden of data processing off device's600 primary processors. Any device equipped with this embodiment wouldbe able to store and transmit data in a highly optimized,bandwidth-efficient format with any other device equipped with thisembodiment.

FIG. 12 is a diagram showing an exemplary system architecture 1200,according to a preferred embodiment of the invention. Incoming trainingdata sets may be received at a customized library generator 1300 thatprocesses training data to produce a customized word library 1201comprising key-value pairs of data words (each comprising a string ofbits) and their corresponding calculated binary Huffman codewords. Theresultant word library 1201 may then be processed by a library optimizer1400 to reduce size and improve efficiency, for example by pruninglow-occurrence data entries or calculating approximate codewords thatmay be used to match more than one data word. A transmissionencoder/decoder 1500 may be used to receive incoming data intended forstorage or transmission, process the data using a word library 1201 toretrieve codewords for the words in the incoming data, and then appendthe codewords (rather than the original data) to an outbound datastream. Each of these components is described in greater detail below,illustrating the particulars of their respective processing and otherfunctions, referring to FIGS. 2-4 .

System 1200 provides near-instantaneous source coding that isdictionary-based and learned in advance from sample training data, sothat encoding and decoding may happen concurrently with datatransmission. This results in computational latency that is near zerobut the data size reduction is comparable to classical compression. Forexample, if N bits are to be transmitted from sender to receiver, thecompression ratio of classical compression is C, the ratio between thedeflation factor of system 1200 and that of multi-pass source coding isp, the classical compression encoding rate is R_(C) bit/s and thedecoding rate is R_(D) bit/s, and the transmission speed is S bit/s, thecompress-send-decompress time will be

$T_{old} = {\frac{N}{R_{C}} + \frac{N}{CS} + \frac{N}{CR_{D}}}$

while the transmit-while-coding time for system 1200 will be (assumingthat encoding and decoding happen at least as quickly as networklatency):

$T_{new} = {\frac{N_{p}}{CS}{so}}$

that the total data transit time improvement factor is

$\frac{T_{old}}{T_{new}} = \frac{\frac{CS}{R_{C}} + 1 + \frac{S}{R_{D}}}{p}$

which presents a savings whenever

${\frac{CS}{R_{C}} + \frac{S}{R_{D}}} > {p - {1.}}$

This is a reasonable scenario given that typical values in real-worldpractice are C=0.32, R_(C)=1.1·10¹², R_(D)=4.2·10¹², S=10¹¹, giving

${{\frac{CS}{R_{C}} + \frac{S}{R_{D}}} = {{0.0}53\ldots}},$

such that system 1200 will outperform the total transit time of the bestcompression technology available as long as its deflation factor is nomore than 5% worse than compression. Such customized dictionary-basedencoding will also sometimes exceed the deflation ratio of classicalcompression, particularly when network speeds increase beyond 100 Gb/s.

The delay between data creation and its readiness for use at a receivingend will be equal to only the source word length t (typically 5-15bytes), divided by the deflation factor C/p and the network speed S,

${i.e.{delay}_{invention}} = \frac{tp}{CS}$

since encoding and decoding occur concurrently with data transmission.On the other hand, the latency associated with classical compression is

${delay}_{priorart} = {\frac{N}{R_{C}} + \frac{N}{CS} + \frac{N}{CR_{D}}}$

where N is the packet/file size. Even with the generous values chosenabove as well as N=512K, t=10, and p=1.05, this results indelay_(invention)≈3.3·10⁻¹⁰ while delay_(priorart)≈1.3·10⁻⁷, a more than400-fold reduction in latency.

A key factor in the efficiency of Huffman coding used by system 1200 isthat key-value pairs be chosen carefully to minimize expected codinglength, so that the average deflation/compression ratio is minimized. Itis possible to achieve the best possible expected code length among allinstantaneous codes using Huffman codes if one has access to the exactprobability distribution of source words of a given desired length fromthe random variable generating them. In practice this is impossible, asdata is received in a wide variety of formats and the random processesunderlying the source data are a mixture of human input, unpredictable(though in principle, deterministic) physical events, and noise. System1200 addresses this by restriction of data types and density estimation;training data is provided that is representative of the type of dataanticipated in “real-world” use of system 1200, which is then used tomodel the distribution of binary strings in the data in order to build aHuffman code word library 1200.

FIG. 13 is a diagram showing a more detailed architecture for acustomized library generator 1300. When an incoming training data set1301 is received, it may be analyzed using a frequency creator 1302 toanalyze for word frequency (that is, the frequency with which a givenword occurs in the training data set). Word frequency may be analyzed byscanning all substrings of bits and directly calculating the frequencyof each substring by iterating over the data set to produce anoccurrence frequency, which may then be used to estimate the rate ofword occurrence in non-training data. A first Huffman binary tree iscreated based on the frequency of occurrences of each word in the firstdataset, and a Huffman codeword is assigned to each observed word in thefirst dataset according to the first Huffman binary tree. Machinelearning may be utilized to improve results by processing a number oftraining data sets and using the results of each training set to refinethe frequency estimations for non-training data, so that the estimationyield better results when used with real-world data (rather than, forexample, being only based on a single training data set that may not bevery similar to a received non-training data set). A second Huffman treecreator 1303 may be utilized to identify words that do not match anyexisting entries in a word library 1201 and pass them to a hybridencoder/decoder 1304, that then calculates a binary Huffman codeword forthe mismatched word and adds the codeword and original data to the wordlibrary 1201 as a new key-value pair. In this manner, customized librarygenerator 1300 may be used both to establish an initial word library1201 from a first training set, as well as expand the word library 1201using additional training data to improve operation.

FIG. 14 is a diagram showing a more detailed architecture for a libraryoptimizer 1400. A pruner 1401 may be used to load a word library 1201and reduce its size for efficient operation, for example by sorting theword library 1201 based on the known occurrence probability of eachkey-value pair and removing low-probability key-value pairs based on aloaded threshold parameter. This prunes low-value data from the wordlibrary to trim the size, eliminating large quantities ofvery-low-frequency key-value pairs such as single-occurrence words thatare unlikely to be encountered again in a data set. Pruning eliminatesthe least-probable entries from word library 1201 up to a giventhreshold, which will have a negligible impact on the deflation factorsince the removed entries are only the least-common ones, while theimpact on word library size will be larger because samples drawn fromasymptotically normal distributions (such as the log-probabilities ofwords generated by a probabilistic finite state machine, a modelwell-suited to a wide variety of real-world data) which occur in tailsof the distribution are disproportionately large in counting measure. Adelta encoder 1402 may be utilized to apply delta encoding to aplurality of words to store an approximate codeword as a value in theword library, for which each of the plurality of source words is a validcorresponding key. This may be used to reduce library size by replacingnumerous key-value pairs with a single entry for the approximatecodeword and then represent actual codewords using the approximatecodeword plus a delta value representing the difference between theapproximate codeword and the actual codeword. Approximate coding isoptimized for low-weight sources such as Golomb coding, run-lengthcoding, and similar techniques. The approximate source words may bechosen by locality-sensitive hashing, so as to approximate Hammingdistance without incurring the intractability of nearest-neighbor-searchin Hamming space. A parametric optimizer 1403 may load configurationparameters for operation to optimize the use of the word library 1201during operation. Best-practice parameter/hyperparameter optimizationstrategies such as stochastic gradient descent, quasi-random gridsearch, and evolutionary search may be used to make optimal choices forall interdependent settings playing a role in the functionality ofsystem 1200. In cases where lossless compression is not required, thedelta value may be discarded at the expense of introducing some limitederrors into any decoded (reconstructed) data.

FIG. 15 is a diagram showing a more detailed architecture for atransmission encoder/decoder 1500. According to various arrangements,transmission encoder/decoder 1500 may be used to deconstruct data forstorage or transmission, or to reconstruct data that has been received,using a word library 1201. A library comparator 1501 may be used toreceive data comprising words or codewords and compare against a wordlibrary 1201 by dividing the incoming stream into substrings of length tand using a fast hash to check word library 1201 for each substring. Ifa substring is found in word library 1201, the corresponding key/value(that is, the corresponding source word or codeword, according towhether the substring used in comparison was itself a word or codeword)is returned and appended to an output stream. If a given substring isnot found in word library 1201, a mismatch handler 1502 and hybridencoder/decoder 1503 may be used to handle the mismatch similarly tooperation during the construction or expansion of word library 1201. Amismatch handler 1502 may be utilized to identify words that do notmatch any existing entries in a word library 1201 and pass them to ahybrid encoder/decoder 1503, that then calculates a binary Huffmancodeword using shorter block-length encoding for the mismatched word andadds the codeword and original data to the word library 1201 as a newkey-value pair. The newly-produced codeword may then be appended to theoutput stream. In arrangements where a mismatch indicator is included ina received data stream, this may be used to preemptively identify asubstring that is not in word library 1201 (for example, if it wasidentified as a mismatch on the transmission end), and handledaccordingly without the need for a library lookup.

FIG. 19 is an exemplary system architecture of a data encoding systemused for cyber security purposes. Much like in FIG. 1 , incoming data101 to be deconstructed is sent to a data deconstruction engine 102,which may attempt to deconstruct the data and turn it into a collectionof codewords using a library manager 103. Codeword storage 106 serves tostore unique codewords from this process, and may be queried by a datareconstruction engine 108 which may reconstruct the original data fromthe codewords, using a library manager 103. However, a cybersecuritygateway 1900 is present, communicating in-between a library manager 103and a deconstruction engine 102, and containing an anomaly detector 1910and distributed denial of service (DDoS) detector 1920. The anomalydetector examines incoming data to determine whether there is adisproportionate number of incoming reference codes that do not matchreference codes in the existing library. A disproportionate number ofnon-matching reference codes may indicate that data is being receivedfrom an unknown source, of an unknown type, or contains unexpected(possibly malicious) data. If the disproportionate number ofnon-matching reference codes exceeds an established threshold orpersists for a certain length of time, the anomaly detector 1910 raisesa warning to a system administrator. Likewise, the DDoS detector 1920examines incoming data to determine whether there is a disproportionateamount of repetitive data. A disproportionate amount of repetitive datamay indicate that a DDoS attack is in progress. If the disproportionateamount of repetitive data exceeds an established threshold or persistsfor a certain length of time, the DDoS detector 1910 raises a warning toa system administrator. In this way, a data encoding system may detectand warn users of, or help mitigate, common cyber-attacks that resultfrom a flow of unexpected and potentially harmful data, or attacks thatresult from a flow of too much irrelevant data meant to slow down anetwork or system, as in the case of a DDoS attack.

FIG. 22 is an exemplary system architecture of a data encoding systemused for data mining and analysis purposes. Much like in FIG. 1 ,incoming data 101 to be deconstructed is sent to a data deconstructionengine 102, which may attempt to deconstruct the data and turn it into acollection of codewords using a library manager 103. Codeword storage106 serves to store unique codewords from this process and may bequeried by a data reconstruction engine 108 which may reconstruct theoriginal data from the codewords, using a library manager 103. A dataanalysis engine 2210, typically operating while the system is otherwiseidle, sends requests for data to the data reconstruction engine 108,which retrieves the codewords representing the requested data fromcodeword storage 106, reconstructs them into the data represented by thecodewords, and send the reconstructed data to the data analysis engine2210 for analysis and extraction of useful data (i.e., data mining).Because the speed of reconstruction is significantly faster thandecompression using traditional compression technologies (i.e.,significantly less decompression latency), this approach makes datamining feasible. Very often, data stored using traditional compressionis not mined precisely because decompression lag makes it unfeasible,especially during shorter periods of system idleness. Increasing thespeed of data reconstruction broadens the circumstances under which datamining of stored data is feasible.

FIG. 24 is an exemplary system architecture of a data encoding systemused for remote software and firmware updates. Software and firmwareupdates typically require smaller, but more frequent, file transfers. Aserver which hosts a software or firmware update 2410 may host anencoding-decoding system 2420, allowing for data to be encoded into, anddecoded from, sourceblocks or codewords, as disclosed in previousfigures. Such a server may possess a software update, operating systemupdate, firmware update, device driver update, or any other form ofsoftware update, which in some cases may be minor changes to a file, butnevertheless necessitate sending the new, completed file to therecipient. Such a server is connected over a network 2430, which isfurther connected to a recipient computer 2440, which may be connectedto a server 2410 for receiving such an update to its system. In thisinstance, the recipient device 2440 also hosts the encoding and decodingsystem 2450, along with a codebook or library of reference codes thatthe hosting server 2410 also shares. The updates are retrieved fromstorage at the hosting server 2410 in the form of codewords, transferredover the network 2430 in the form of codewords, and reconstructed on thereceiving computer 2440. In this way, a far smaller file size, andsmaller total update size, may be sent over a network. The receivingcomputer 2440 may then install the updates on any number of targetcomputing devices 2460 a-n, using a local network or otherhigh-bandwidth connection.

FIG. 26 is an exemplary system architecture of a data encoding systemused for large-scale software installation such as operating systems.Large-scale software installations typically require very large, butinfrequent, file transfers. A server which hosts an installable software2610 may host an encoding-decoding system 2620, allowing for data to beencoded into, and decoded from, sourceblocks or codewords, as disclosedin previous figures. The files for the large scale software installationare hosted on the server 2610, which is connected over a network 2630 toa recipient computer 2640. In this instance, the encoding and decodingsystem 2650 a-n is stored on or connected to one or more target devices2660 a-n, along with a codebook or library of reference codes that thehosting server 2610 shares. The software is retrieved from storage atthe hosting server 2610 in the form of codewords and transferred overthe network 2630 in the form of codewords to the receiving computer2640. However, instead of being reconstructed at the receiving computer2640, the codewords are transmitted to one or more target computingdevices, and reconstructed and installed directly on the target devices2660 a-n. In this way, a far smaller file size, and smaller total updatesize, may be sent over a network or transferred between computingdevices, even where the network 2630 between the receiving computer 2640and target devices 2660 a-n is low bandwidth, or where there are manytarget devices 2660 a-n.

FIG. 28 is a block diagram of an exemplary system architecture 2800 of acodebook training system for a data encoding system, according to anembodiment. According to this embodiment, two separate machines may beused for encoding 2810 and decoding 2820. Much like in FIG. 1 , incomingdata 101 to be deconstructed is sent to a data deconstruction engine 102residing on encoding machine 2810, which may attempt to deconstruct thedata and turn it into a collection of codewords using a library manager103. Codewords may be transmitted 2840 to a data reconstruction engine108 residing on decoding machine 2820, which may reconstruct theoriginal data from the codewords, using a library manager 103. However,according to this embodiment, a codebook training module 2830 is presenton the decoding machine 2810, communicating in-between a library manager103 and a deconstruction engine 102. According to other embodiments,codebook training module 2830 may reside instead on decoding machine2820 if the machine has enough computing resources available; whichmachine the module 2830 is located on may depend on the system user'sarchitecture and network structure. Codebook training module 2830 maysend requests for data to the data reconstruction engine 2810, whichroutes incoming data 101 to codebook training module 2830. Codebooktraining module 2830 may perform analyses on the requested data in orderto gather information about the distribution of incoming data 101 aswell as monitor the encoding/decoding model performance. Additionally,codebook training module 2830 may also request and receive device data2860 to supervise network connected devices and their processes and,according to some embodiments, to allocate training resources whenrequested by devices running the encoding system. Devices may include,but are not limited to, encoding and decoding machines, trainingmachines, sensors, mobile computer devices, and Internet-of-things(“IoT”) devices. Based on the results of the analyses, the codebooktraining module 2830 may create a new training dataset from a subset ofthe requested data in order to counteract the effects of data drift onthe encoding/decoding models, and then publish updated 2850 codebooks toboth the encoding machine 2810 and decoding machine 2820.

FIG. 29 is a block diagram of an exemplary architecture for a codebooktraining module 2900, according to an embodiment. According to theembodiment, a data collector 2910 is present which may send requests forincoming data 2905 to a data deconstruction engine 102 which may receivethe request and route incoming data to codebook training module 2900where it may be received by data collector 2910. Data collector 2910 maybe configured to request data periodically such as at schedule timeintervals, or for example, it may be configured to request data after acertain amount of data has been processed through the encoding machine2810 or decoding machine 2820. The received data may be a plurality ofsourceblocks, which are a series of binary digits, originating from asource packet otherwise referred to as a datagram. The received data maycompiled into a test dataset and temporarily stored in a cache 2970.Once stored, the test dataset may be forwarded to a statistical analysisengine 2920 which may utilize one or more algorithms to determine theprobability distribution of the test dataset. Best-practice probabilitydistribution algorithms such as Kullback-Leibler divergence, adaptivewindowing, and Jensen-Shannon divergence may be used to compute theprobability distribution of training and test datasets. A monitoringdatabase 2930 may be used to store a variety of statistical data relatedto training datasets and model performance metrics in one place tofacilitate quick and accurate system monitoring capabilities as well asassist in system debugging functions. For example, the original orcurrent training dataset and the calculated probability distribution ofthis training dataset used to develop the current encoding and decodingalgorithms may be stored in monitor database 2930.

Since data drifts involve statistical change in the data, the bestapproach to detect drift is by monitoring the incoming data'sstatistical properties, the model's predictions, and their correlationwith other factors. After statistical analysis engine 2920 calculatesthe probability distribution of the test dataset it may retrieve frommonitor database 2930 the calculated and stored probability distributionof the current training dataset. It may then compare the two probabilitydistributions of the two different datasets in order to verify if thedifference in calculated distributions exceeds a predetermineddifference threshold. If the difference in distributions does not exceedthe difference threshold, that indicates the test dataset, and thereforethe incoming data, has not experienced enough data drift to cause theencoding/decoding system performance to degrade significantly, whichindicates that no updates are necessary to the existing codebooks.However, if the difference threshold has been surpassed, then the datadrift is significant enough to cause the encoding/decoding systemperformance to degrade to the point where the existing models andaccompanying codebooks need to be updated. According to an embodiment,an alert may be generated by statistical analysis engine 2920 if thedifference threshold is surpassed or if otherwise unexpected behaviorarises.

In the event that an update is required, the test dataset stored in thecache 2970 and its associated calculated probability distribution may besent to monitor database 2930 for long term storage. This test datasetmay be used as a new training dataset to retrain the encoding anddecoding algorithms 2940 used to create new sourceblocks based upon thechanged probability distribution. The new sourceblocks may be sent outto a library manager 2915 where the sourceblocks can be assigned newcodewords. Each new sourceblock and its associated codeword may then beadded to a new codebook and stored in a storage device. The new andupdated codebook may then be sent back 2925 to codebook training module2900 and received by a codebook update engine 2950. Codebook updateengine 2950 may temporarily store the received updated codebook in thecache 2970 until other network devices and machines are ready, at whichpoint codebook update engine 2950 will publish the updated codebooks2945 to the necessary network devices.

A network device manager 2960 may also be present which may request andreceive network device data 2935 from a plurality of network connecteddevices and machines. When the disclosed encoding system and codebooktraining system 2800 are deployed in a production environment, upstreamprocess changes may lead to data drift, or other unexpected behavior.For example, a sensor being replaced that changes the units ofmeasurement from inches to centimeters, data quality issues such as abroken sensor always reading zero, and covariate shift which occurs whenthere is a change in the distribution of input variables from thetraining set. These sorts of behavior and issues may be determined fromthe received device data 2935 in order to identify potential causes ofsystem error that is not related to data drift and therefore does notrequire an updated codebook. This can save network resources from beingunnecessarily used on training new algorithms as well as alert systemusers to malfunctions and unexpected behavior devices connected to theirnetworks. Network device manager 2960 may also utilize device data 2935to determine available network resources and device downtime or periodsof time when device usage is at its lowest. Codebook update engine 2950may request network and device availability data from network devicemanager 2960 in order to determine the most optimal time to transmitupdated codebooks (i.e., trained libraries) to encoder and decoderdevices and machines.

FIG. 30 is a block diagram of another embodiment of the codebooktraining system using a distributed architecture and a modified trainingmodule. According to an embodiment, there may be a server whichmaintains a master supervisory process over remote training deviceshosting a master training module 3010 which communicates via a network3020 to a plurality of connected network devices 3030 a-n. The servermay be located at the remote training end such as, but not limited to,cloud-based resources, a user-owned data center, etc. The mastertraining module located on the server operates similarly to the codebooktraining module disclosed in FIG. 29 above, however, the server 3010utilizes the master training module via the network device manager 2960to farm out training resources to network devices 3030 a-n. The server3010 may allocate resources in a variety of ways, for example,round-robin, priority-based, or other manner, depending on the userneeds, costs, and number of devices running the encoding/decodingsystem. Server 3010 may identify elastic resources which can be employedif available to scale up training when the load becomes too burdensome.On the network devices 3030 a-n may be present a lightweight version ofthe training module 3040 that trades a little suboptimality in thecodebook for training on limited machinery and/or makes training happenin low-priority threads to take advantage of idle time. In this way thetraining of new encoding/decoding algorithms may take place in adistributed manner which allows data gathering or generating devices toprocess and train on data gathered locally, which may improve systemlatency and optimize available network resources.

FIG. 32 is an exemplary system architecture for an encoding system withmultiple codebooks. A data set to be encoded 3201 is sent to asourcepacket buffer 3202. The sourcepacket buffer is an array whichstores the data which is to be encoded and may contain a plurality ofsourcepackets. Each sourcepacket is routed to a codebook selector 3300,which retrieves a list of codebooks from a codebook database 3203. Thesourcepacket is encoded using the first codebook on the list via anencoder 3204, and the output is stored in an encoded sourcepacket buffer3205. The process is repeated with the same sourcepacket using eachsubsequent codebook on the list until the list of codebooks is exhausted3206, at which point the most compact encoded version of thesourcepacket is selected from the encoded sourcepacket buffer 3205 andsent to an encoded data set buffer 3208 along with the ID of thecodebook used to produce it. The sourcepacket buffer 3202 is determinedto be exhausted 3207, a notification is sent to a combiner 3400, whichretrieves all of the encoded sourcepackets and codebook IDs from theencoded data set buffer 3208 and combines them into a single file foroutput.

According to an embodiment, the list of codebooks used in encoding thedata set may be consolidated to a single codebook which is provided tothe combiner 3400 for output along with the encoded sourcepackets andcodebook IDs. In this case, the single codebook will contain the datafrom, and codebook IDs of, each of the codebooks used to encode the dataset. This may provide a reduction in data transfer time, although it isnot required since each sourcepacket (or sourceblock) will contain areference to a specific codebook ID which references a codebook that canbe pulled from a database or be sent alongside the encoded data to areceiving device for the decoding process.

In some embodiments, each sourcepacket of a data set 3201 arriving atthe encoder 3204 is encoded using a different sourceblock length.Changing the sourceblock length changes the encoding output of a givencodebook. Two sourcepackets encoded with the same codebook but usingdifferent sourceblock lengths would produce different encoded outputs.Therefore, changing the sourceblock length of some or all sourcepacketsin a data set 3201 provides additional security. Even if the codebookwas known, the sourceblock length would have to be known or derived foreach sourceblock in order to decode the data set 3201. Changing thesourceblock length may be used in conjunction with the use of multiplecodebooks.

FIG. 33 is a flow diagram describing an exemplary algorithm for encodingof data using multiple codebooks. A data set is received for encoding3301, the data set comprising a plurality of sourcepackets. Thesourcepackets are stored in a sourcepacket buffer 3302. A list ofcodebooks to be used for multiple codebook encoding is retrieved from acodebook database (which may contain more codebooks than are containedin the list) and the codebook IDs for each codebook on the list arestored as an array 3303. The next sourcepacket in the sourcepacketbuffer is retrieved from the sourcepacket buffer for encoding 3304. Thesourcepacket is encoded using the codebook in the array indicated by acurrent array pointer 3305. The encoded sourcepacket and length of theencoded sourcepacket is stored in an encoded sourcepacket buffer 3306.If the length of the most recently stored sourcepacket is the shortestin the buffer 3607, an index in the buffer is updated to indicate thatthe codebook indicated by the current array pointer is the mostefficient codebook in the buffer for that sourcepacket. If the length ofthe most recently stored sourcepacket is not the shortest in the buffer3607, the index in the buffer is not updated because a previous codebookused to encode that sourcepacket was more efficient 3309. The currentarray pointer is iterated to select the next codebook in the list 3310.If the list of codebooks has not been exhausted 3311, the process isrepeated for the next codebook in the list, starting at step 3305. Ifthe list of codebooks has been exhausted 3311, the encoded sourcepacketin the encoded sourcepacket buffer (the most compact version) and thecodebook ID for the codebook that encoded it are added to an encodeddata set buffer 3312 for later combination with other encodedsourcepackets from the same data set. At that point, the sourcepacketbuffer is checked to see if any sourcepackets remain to be encoded 3313.If the sourcepacket buffer is not exhausted, the next sourcepacket isretrieved 3304 and the process is repeated starting at step 3304. If thesourcepacket buffer is exhausted 3313, the encoding process ends 3314.In some embodiments, rather than storing the encoded sourcepacket itselfin the encoded sourcepacket buffer, a universal unique identification(UUID) is assigned to each encoded sourcepacket, and the UUID is storedin the encoded sourcepacket buffer instead of the entire encodedsourcepacket.

FIG. 34 is a diagram showing an exemplary control byte used to combinesourcepackets encoded with multiple codebooks. In this embodiment, acontrol byte 3401 (i.e., a series of 8 bits) is inserted at the before(or after, depending on the configuration) the encoded sourcepacket withwhich it is associated, and provides information about the codebook thatwas used to encode the sourcepacket. In this way, sourcepackets of adata set encoded using multiple codebooks can be combined into a datastructure comprising the encoded sourcepackets, each with a control bytethat tells the system how the sourcepacket can be decoded. The datastructure may be of numerous forms, but in an embodiment, the datastructure comprises a continuous series of control bytes followed by thesourcepacket associated with the control byte. In some embodiments, thedata structure will comprise a continuous series of control bytesfollowed by the UUID of the sourcepacket associated with the controlbyte (and not the encoded sourcepacket, itself). In some embodiments,the data structure may further comprise a UUID inserted to identify thecodebook used to encode the sourcepacket, rather than identifying thecodebook in the control byte. Note that, while a very short control code(one byte) is used in this example, the control code may be of anylength, and may be considerably longer than one byte in cases where thesourceblocks size is large or in cases where a large number of codebookshave been used to encode the sourcepacket or data set.

In this embodiment, for each bit location 3402 of the control byte 3401,a data bit or combinations of data bits 3403 provide informationnecessary for decoding of the sourcepacket associated with the controlbyte. Reading in reverse order of bit locations, the first bit N(location 7) indicates whether the entire control byte is used or not.If a single codebook is used to encode all sourcepackets in the dataset, N is set to 0, and bits 3 to 0 of the control byte 3401 areignored. However, where multiple codebooks are used, N is set to 1 andall 8 bits of the control byte 3401 are used. The next three bits RRR(locations 6 to 4) are a residual count of the number of bits that werenot used in the last byte of the sourcepacket. Unused bits in the lastbyte of a sourcepacket can occur depending on the sourceblock size usedto encode the sourcepacket. The next bit I (location 3) is used toidentify the codebook used to encode the sourcepacket. If bit I is 0,the next three bits CCC (locations 2 to 0) provide the codebook ID usedto encode the sourcepacket. The codebook ID may take the form of acodebook cache index, where the codebooks are stored in an enumeratedcache. If bit I is 1, then the codebook is identified using a four-byteUUID that follows the control byte.

FIG. 35 is a diagram showing an exemplary codebook shuffling method. Inthis embodiment, rather than selecting codebooks for encoding based ontheir compaction efficiency, codebooks are selected either based on arotating list or based on a shuffling algorithm. The methodology of thisembodiment provides additional security to compacted data, as the datacannot be decoded without knowing the precise sequence of codebooks usedto encode any given sourcepacket or data set.

Here, a list of six codebooks is selected for shuffling, each identifiedby a number from 1 to 6 3501 a. The list of codebooks is sent to arotation or shuffling algorithm 3502 and reorganized according to thealgorithm 3501 b. The first six of a series of sourcepackets, eachidentified by a letter from A to E, 3503 is each encoded by one of thealgorithms, in this case A is encoded by codebook 1, B is encoded bycodebook 6, C is encoded by codebook 2, D is encoded by codebook 4, E isencoded by codebook 13 A is encoded by codebook 5. The encodedsourcepackets 3503 and their associated codebook identifiers 3501 b arecombined into a data structure 3504 in which each encoded sourcepacketis followed by the identifier of the codebook used to encode thatparticular sourcepacket.

According to an embodiment, the codebook rotation or shuffling algorithm3502 may produce a random or pseudo-random selection of codebooks basedon a function. Some non-limiting functions that may be used forshuffling include:

-   -   1. given a function f(n) which returns a codebook according to        an input parameter n in the range 1 to N are, and given t the        number of the current sourcepacket or sourceblock: f(t*M modulo        p), where M is an arbitrary multiplying factor (1<=M<=p−1) which        acts as a key, and p is a large prime number less than or equal        to N;    -   2. f(A{circumflex over ( )}t modulo p), where A is a base        relatively prime to p−1 which acts as a key, and p is a large        prime number less than or equal to N;    -   3. f(floor(t*x) modulo N), and x is an irrational number chosen        randomly to act as a key;    -   4. f(t XOR K) where the XOR is performed bit-wise on the binary        representations of t and a key K with same number of bits in its        representation of N. The function f(n) may return the nth        codebook simply by referencing the nth element in a list of        codebooks, or it could return the nth codebook given by a        formula chosen by a user.

In one embodiment, prior to transmission, the endpoints (users ordevices) of a transmission agree in advance about the rotation list orshuffling function to be used, along with any necessary input parameterssuch as a list order, function code, cryptographic key, or otherindicator, depending on the requirements of the type of list or functionbeing used. Once the rotation list or shuffling function is agreed, theendpoints can encode and decode transmissions from one another using theencodings set forth in the current codebook in the rotation or shuffleplus any necessary input parameters.

In some embodiments, the shuffling function may be restricted topermutations within a set of codewords of a given length.

Note that the rotation or shuffling algorithm is not limited to cyclingthrough codebooks in a defined order. In some embodiments, the order maychange in each round of encoding. In some embodiments, there may be norestrictions on repetition of the use of codebooks.

In some embodiments, codebooks may be chosen based on some combinationof compaction performance and rotation or shuffling. For example,codebook shuffling may be repeatedly applied to each sourcepacket untila codebook is found that meets a minimum level of compaction for thatsourcepacket. Thus, codebooks are chosen randomly or pseudo-randomly foreach sourcepacket, but only those that produce encodings of thesourcepacket better than a threshold will be used.

FIG. 36 is a block diagram of an exemplary system architecture forencoding data using mismatch probability estimates, according to anembodiment. According to an embodiment, encoder/decoder system withmismatch probability estimation capability 3600 comprises a statisticalanalyzer 3610, a codebook generator 3620, a reference codebook 3602, andan encoder/decoder 3630.

Entropy encoding methods (also known as entropy coding methods) arelossless data compression methods which replace fixed-length data inputswith variable-length prefix-free codewords based on the frequency oftheir occurrence within a given distribution. This reduces the number ofbits required to store the data inputs, limited by the entropy of thetotal data set. The most well-known entropy encoding method is Huffmancoding, which will be used in the examples herein.

Because any lossless data compression method must have a code lengthsufficient to account for the entropy of the data set, entropy encodingis most compact where the entropy of the data set is small. However,smaller entropy in a data set means that, by definition, the data setcontains fewer variations of the data. So, the smaller the entropy of adata set used to create a codebook using an entropy encoding method, thelarger is the probability that some piece of data to be encoded will notbe found in that codebook. Adding new data to the codebook leads toinefficiencies that undermine the use of a low-entropy data set tocreate the codebook.

System 3600 receives a training data set 3601 comprising one or moresourcepackets of data, wherein each of the one or more sourcepackets ofdata may further comprise a plurality of sourceblocks. Ideally, trainingdata set 3601 will be selected to closely match data that will later beinput into the system for encoding (a low-entropy data set relative toexpected data to be encoded). As sourceblocks of training data set data3601 are processed, statistical analyzer 3610 uses frequency calculator3611 to keep track of sourceblock frequency, which is the frequency atwhich each distinct sourceblock occurs in the training data set. Oncethe training data set 3601 has been fully processed and the sourceblockfrequency is known, system 3600 has sufficient information to create acodebook using an entropy encoding method such as Huffman coding. Whilea codebook can be created at this point, the codebook will not containcodewords for sourceblocks that were either not encountered in thetraining data sets 3601, or that were included in the training data sets3601 but were pruned from the codebook for various reasons (as oneexample, sourceblocks that do not appear frequently enough in a givendata set may be pruned for purposes of efficiency or space-saving).

To address the problem of mismatched sourceblocks during encoding (i.e.,sourceblocks in data to be encoded which do not have a codeword in thecodebook), mismatch probability estimation is used, wherein theprobability of encountering data that is not in the codebook iscalculated in advance, and a special “mismatch codework” is incorporatedinto the codebook (the primary encoding algorithm) to represent theexpected frequency of encountering previously-unencounteredsourceblocks. When a previously-unencountered sourceblock is encounteredduring encoding, attempting to encode the sourceblock using the codebookresults in the mismatch codeword, which triggers a secondary encodingalgorithm to encode that sourceblock. The secondary encoding algorithmmay result in a less-than-optimal encoding of thepreviously-unencountered data, but the efficiencies of using alow-entropy primary encoding make up for the inefficiencies of thesecondary encoding algorithm. Because the use of the secondary encodingalgorithm has been accounted for in the codebook (the primary encodingalgorithm) by the mismatch probability estimation, the overallefficiency of compaction is improved over other entropy encodingmethods.

Mismatch probability estimator 3612 calculates the probability that asourceblock to be encoded will not be in the codebook generated from thetraining data. This probability is difficult to estimate because it isthe probability that a sourceblock is not one which was seen in thetraining data (i.e., the system needs to estimate the probability of apreviously-unseen event). Several algorithms for calculating themismatch probability follow. The mismatch probability in thesealgorithms is defined as q. These algorithms are intended to beexemplary, and not exclusive of other algorithms that could be used tocalculate this probability.

In a first algorithm, q is taken to be the number M of times a mismatchoccurred during training (i.e., when a previous-unobserved sourceblockappeared in the training data), dividing by the total number N ofsourceblocks observed during training, i.e., q=M/N. However, for manytraining data sets, a static q=M/N may not be an accurate estimate forq, as the mismatch frequency may fall with time as training data isingested, resulting in a q that is too high. This is likely to be thecase where the training and real-world data are drawn from the same datatype.

A second algorithm that improves on the first uses a sum ofprobabilities to calculate q. Suppose that sourceblocks S₁, S₂, . . . ,S_(N) are observed during training. For j=1, . . . , N, let the variableX_(j) denote the indicator of the event that sourceblock S_(j) is amismatch, i.e.,

$X_{j} = \left\{ \begin{matrix}{{1{if}S_{j}} \notin \left\{ {{S_{i}:1} \leq i < j} \right\}} \\{0{otherwise}}\end{matrix} \right.$

Then we can write q=M/N=(Σ_(j=1) ^(N)X_(j))/N.

A third algorithm that improves on the second, employs a modifiedexponentially-weighted moving average (EWMA) to calculate changes in qover time:

$\mu_{j} = \left\{ \begin{matrix}{{1{if}j} = 0} \\{{{\left( {1 - \beta_{j}} \right)\mu_{j - 1}} + {\beta_{j}X_{j}{if}j}} > 0}\end{matrix} \right.$

If β_(j), a quantity between 0 and 1, were constant (i.e., not dependingon j), then this is a classical EWMA. However, there are two issues tobalance in choosing β_(j): a value too close to 1 causes extremevolatility in the estimate μ_(j), since it will depend only on veryrecent occurrences/nonoccurrences of mismatches; and a value too closeto 0 will cause difficult round-off errors or else cause the estimate todepend on very early training data (when mismatch frequencies will bemisleadingly high). Therefore, we take β₃=C log(j)/j (and β₁=1 to avoidinitialization problems), for some constant C. In practice, we haveobserved C=1 to be a good choice here, though it is by no means the onlypossibility, and some applications with particularly stable or unstablemismatch distributions will benefit from a different value. The effectof this choice is to cause the mismatch probability estimate μ_(j) todepend only on the recent O(1/log(j)) fraction of the data whensourceblock j is observed, a quantity tending to zero slowly.

Two additional adjustments may be made to deal with certain cases.First, when training begins, the statistic μ_(j) is highly volatile,resulting in poor estimates if the training data is very small.Therefore, an adjustment to the algorithm for this case is to monitorthe sample standard deviation σ_(j) of μ_(j) and use the aforementionedM/N estimate until σ_(j) falls below some pre-set tolerance, for examplethe condition that σ_(j)/μ_(j)<10%. This value of 10% can be replacedwith another value if experimentation shows that a difference value iswarranted for a particular data type. Second, the quantity μ_(j) tendsto be a slight overestimate because it will fall over time duringtraining, so it may be biased slightly above the true mismatchprobability. Therefore, am adjustment to the algorithm for this case isto use the smallest recent value of μ_(j) instead of μ_(j) itself, i.e.,

$\mu_{j}^{\prime} = {\min\limits_{{j - B} \leq i \leq j}\mu_{i}}$

where B is a “windowing” parameter reflecting how far back in thehistory of the training process to incorporate in the estimate, andnegative indices are ignored. It may be useful in some circumstances totake a variable value for B=B_(j) instead of a constant, a reasonablechoice being B_(j)=j/(C log j), the effective window size for the EWMAdiscussed above.

After the mismatch probability estimate is made, codebook generator 3620generates a codebook using entropy encoder 3621. Entropy encoder 3621uses an entropy encoding method to create a codebook based on thefrequency of occurrences of each sourceblock in the training data set,including the estimated frequency of occurrence of mismatchedsourceblocks, for which a special “mismatch codeword” is inserted intothe codebook. The resulting codebook is stored in a database 3602, whichis accessed by encoder/decoder 3630 to encode data to be encoded 3603.When a mismatch occurs and the mismatch codeword is returned, mismatchhandler 3631 receives the mismatched sourceblock and encodes it using asecondary encoding method, inserting the secondary encoding into theencoded data stream and returning the encoding process to encoding usingthe codebook (the primary encoding method).

DETAILED DESCRIPTION OF EXEMPLARY ASPECTS

Since the library consists of re-usable building sourceblocks, and theactual data is represented by reference codes to the library, the totalstorage space of a single set of data would be much smaller thanconventional methods, wherein the data is stored in its entirety. Themore data sets that are stored, the larger the library becomes, and themore data can be stored in reference code form.

As an analogy, imagine each data set as a collection of printed booksthat are only occasionally accessed. The amount of physical shelf spacerequired to store many collections would be quite large and is analogousto conventional methods of storing every single bit of data in everydata set. Consider, however, storing all common elements within andacross books in a single library, and storing the books as referencescodes to those common elements in that library. As a single book isadded to the library, it will contain many repetitions of words andphrases. Instead of storing the whole words and phrases, they are addedto a library, and given a reference code, and stored as reference codes.At this scale, some space savings may be achieved, but the referencecodes will be on the order of the same size as the words themselves. Asmore books are added to the library, larger phrases, quotations, andother words patterns will become common among the books. The larger theword patterns, the smaller the reference codes will be in relation tothem as not all possible word patterns will be used. As entirecollections of books are added to the library, sentences, paragraphs,pages, or even whole books will become repetitive. There may be manyduplicates of books within a collection and across multiple collections,many references and quotations from one book to another, and much commonphraseology within books on particular subjects. If each unique page ofa book is stored only once in a common library and given a referencecode, then a book of 1,000 pages or more could be stored on a fewprinted pages as a string of codes referencing the proper full-sizedpages in the common library. The physical space taken up by the bookswould be dramatically reduced. The more collections that are added, thegreater the likelihood that phrases, paragraphs, pages, or entire bookswill already be in the library, and the more information in eachcollection of books can be stored in reference form. Accessing entirecollections of books is then limited not by physical shelf space, but bythe ability to reprint and recycle the books as needed for use.

The projected increase in storage capacity using the method hereindescribed is primarily dependent on two factors: 1) the ratio of thenumber of bits in a block to the number of bits in the reference code,and 2) the amount of repetition in data being stored by the system.

With respect to the first factor, the number of bits used in thereference codes to the sourceblocks must be smaller than the number ofbits in the sourceblocks themselves in order for any additional datastorage capacity to be obtained. As a simple example, 16-bitsourceblocks would require 2¹⁶, or 65536, unique reference codes torepresent all possible patterns of bits. If all possible 65536 blockspatterns are utilized, then the reference code itself would also need tocontain sixteen bits in order to refer to all possible 65,536 blockspatterns. In such case, there would be no storage savings. However, ifonly 16 of those block patterns are utilized, the reference code can bereduced to 4 bits in size, representing an effective compression of 4times (16 bits/4 bits=4) versus conventional storage. Using a typicalblock size of 512 bytes, or 4,096 bits, the number of possible blockpatterns is 2^(4,096), which for all practical purposes is unlimited. Atypical hard drive contains one terabyte (TB) of physical storagecapacity, which represents 1,953,125,000, or roughly 2³¹, 512 byteblocks. Assuming that 1 TB of unique 512-byte sourceblocks werecontained in the library, and that the reference code would thus need tobe 31 bits long, the effective compression ratio for stored data wouldbe on the order of 132 times (4,096/31≈132) that of conventionalstorage.

With respect to the second factor, in most cases it could be assumedthat there would be sufficient repetition within a data set such that,when the data set is broken down into sourceblocks, its size within thelibrary would be smaller than the original data. However, it isconceivable that the initial copy of a data set could require somewhatmore storage space than the data stored in a conventional manner, if allor nearly all sourceblocks in that set were unique. For example,assuming that the reference codes are 1/10^(th) the size of a full-sizedcopy, the first copy stored as sourceblocks in the library would need tobe 1.1 megabytes (MB), (1 MB for the complete set of full-sizedsourceblocks in the library and 0.1 MB for the reference codes).However, since the sourceblocks stored in the library are universal, themore duplicate copies of something you save, the greater efficiencyversus conventional storage methods. Conventionally, storing 10 copiesof the same data requires 10 times the storage space of a single copy.For example, ten copies of a 1 MB file would take up 10 MB of storagespace. However, using the method described herein, only a singlefull-sized copy is stored, and subsequent copies are stored as referencecodes. Each additional copy takes up only a fraction of the space of thefull-sized copy. For example, again assuming that the reference codesare 1/10^(th) the size of the full-size copy, ten copies of a 1 MB filewould take up only 2 MB of space (1 MB for the full-sized copy, and 0.1MB each for ten sets of reference codes). The larger the library, themore likely that part or all of incoming data will duplicatesourceblocks already existing in the library.

The size of the library could be reduced in a manner similar to storageof data. Where sourceblocks differ from each other only by a certainnumber of bits, instead of storing a new sourceblock that is verysimilar to one already existing in the library, the new sourceblockcould be represented as a reference code to the existing sourceblock,plus information about which bits in the new block differ from theexisting block. For example, in the case where 512 byte sourceblocks arebeing used, if the system receives a new sourceblock that differs byonly one bit from a sourceblock already existing in the library, insteadof storing a new 512 byte sourceblock, the new sourceblock could bestored as a reference code to the existing sourceblock, plus a referenceto the bit that differs. Storing the new sourceblock as a reference codeplus changes would require only a few bytes of physical storage spaceversus the 512 bytes that a full sourceblock would require. Thealgorithm could be optimized to store new sourceblocks in this referencecode plus changes form unless the changes portion is large enough thatit is more efficient to store a new, full sourceblock.

It will be understood by one skilled in the art that transfer andsynchronization of data would be increased to the same extent as forstorage. By transferring or synchronizing reference codes instead offull-sized data, the bandwidth requirements for both types of operationsare dramatically reduced.

In addition, the method described herein is inherently a form ofencryption. When the data is converted from its full form to referencecodes, none of the original data is contained in the reference codes.Without access to the library of sourceblocks, it would be impossible tore-construct any portion of the data from the reference codes. Thisinherent property of the method described herein could obviate the needfor traditional encryption algorithms, thereby offsetting most or all ofthe computational cost of conversion of data back and forth to referencecodes. In theory, the method described herein should not utilize anyadditional computing power beyond traditional storage using encryptionalgorithms. Alternatively, the method described herein could be inaddition to other encryption algorithms to increase data security evenfurther.

In other embodiments, additional security features could be added, suchas: creating a proprietary library of sourceblocks for proprietarynetworks, physical separation of the reference codes from the library ofsourceblocks, storage of the library of sourceblocks on a removabledevice to enable easy physical separation of the library and referencecodes from any network, and incorporation of proprietary sequences ofhow sourceblocks are read and the data reassembled.

FIG. 7 is a diagram showing an example of how data might be convertedinto reference codes using an aspect of an embodiment 700. As data isreceived 701, it is read by the processor in sourceblocks of a sizedynamically determined by the previously disclosed sourceblock sizeoptimizer 410. In this example, each sourceblock is 16 bits in length,and the library 702 initially contains three sourceblocks with referencecodes 00, 01, and 10. The entry for reference code 11 is initiallyempty. As each 16 bit sourceblock is received, it is compared with thelibrary. If that sourceblock is already contained in the library, it isassigned the corresponding reference code. So, for example, as the firstline of data (0000 0011 0000 0000) is received, it is assigned thereference code (01) associated with that sourceblock in the library. Ifthat sourceblock is not already contained in the library, as is the casewith the third line of data (0000 1111 0000 0000) received in theexample, that sourceblock is added to the library and assigned areference code, in this case 11. The data is thus converted 703 to aseries of reference codes to sourceblocks in the library. The data isstored as a collection of codewords, each of which contains thereference code to a sourceblock and information about the location ofthe sourceblocks in the data set. Reconstructing the data is performedby reversing the process. Each stored reference code in a datacollection is compared with the reference codes in the library, thecorresponding sourceblock is read from the library, and the data isreconstructed into its original form.

FIG. 8 is a method diagram showing the steps involved in using anembodiment 800 to store data. As data is received 801, it would bedeconstructed into sourceblocks 802, and passed 803 to the librarymanagement module for processing. Reference codes would be received back804 from the library management module and could be combined withlocation information to create codewords 805, which would then be stored806 as representations of the original data.

FIG. 9 is a method diagram showing the steps involved in using anembodiment 900 to retrieve data. When a request for data is received901, the associated codewords would be retrieved 902 from the library.The codewords would be passed 903 to the library management module, andthe associated sourceblocks would be received back 904. Upon receipt,the sourceblocks would be assembled 905 into the original data using thelocation data contained in the codewords, and the reconstructed datawould be sent out 906 to the requestor.

FIG. 10 is a method diagram showing the steps involved in using anembodiment 1000 to encode data. As sourceblocks are received 1001 fromthe deconstruction engine, they would be compared 1002 with thesourceblocks already contained in the library. If that sourceblockalready exists in the library, the associated reference code would bereturned 1005 to the deconstruction engine. If the sourceblock does notalready exist in the library, a new reference code would be created 1003for the sourceblock. The new reference code and its associatedsourceblock would be stored 1004 in the library, and the reference codewould be returned to the deconstruction engine.

FIG. 11 is a method diagram showing the steps involved in using anembodiment 1100 to decode data. As reference codes are received 1101from the reconstruction engine, the associated sourceblocks areretrieved 1102 from the library, and returned 1103 to the reconstructionengine.

FIG. 16 is a method diagram illustrating key system functionalityutilizing an encoder and decoder pair, according to a preferredembodiment. In a first step 1601, at least one incoming data set may bereceived at a customized library generator 1300 that then 1602 processesdata to produce a customized word library 1201 comprising key-valuepairs of data words (each comprising a string of bits) and theircorresponding calculated binary Huffman codewords. A subsequent datasetmay be received and compared to the word library 1603 to determine theproper codewords to use in order to encode the dataset. Words in thedataset are checked against the word library and appropriate encodingsare appended to a data stream 1604. If a word is mismatched within theword library and the dataset, meaning that it is present in the datasetbut not the word library, then a mismatched code is appended, followedby the unencoded original word. If a word has a match within the wordlibrary, then the appropriate codeword in the word library is appendedto the data stream. Such a data stream may then be stored or transmitted1605 to a destination as desired. For the purposes of decoding, analready-encoded data stream may be received and compared 1606, andun-encoded words may be appended to a new data stream 1607 depending onword matches found between the encoded data stream and the word librarythat is present. A matching codeword that is found in a word library isreplaced with the matching word and appended to a data stream, and amismatch code found in a data stream is deleted and the followingunencoded word is re-appended to a new data stream, the inverse of theprocess of encoding described earlier. Such a data stream may then bestored or transmitted 1608 as desired.

FIG. 17 is a method diagram illustrating possible use of a hybridencoder/decoder to improve the compression ratio, according to apreferred aspect. A second Huffman binary tree may be created 1701,having a shorter maximum length of codewords than a first Huffman binarytree 1602, allowing a word library to be filled with every combinationof codeword possible in this shorter Huffman binary tree 1702. A wordlibrary may be filled with these Huffman codewords and words from adataset 1702, such that a hybrid encoder/decoder 1304, 1503 may receiveany mismatched words from a dataset for which encoding has beenattempted with a first Huffman binary tree 1703, 1604 and parsepreviously mismatched words into new partial codewords (that is,codewords that are each a substring of an original mismatched codeword)using the second Huffman binary tree 1704. In this way, an incompleteword library may be supplemented by a second word library. New codewordsattained in this way may then be returned to a transmission encoder1705, 1500. In the event that an encoded dataset is received fordecoding, and there is a mismatch code indicating that additional codingis needed, a mismatch code may be removed and the unencoded word used togenerate a new codeword as before 1706, so that a transmission encoder1500 may have the word and newly generated codeword added to its wordlibrary 1707, to prevent further mismatching and errors in encoding anddecoding.

It will be recognized by a person skilled in the art that the methodsdescribed herein can be applied to data in any form. For example, themethod described herein could be used to store genetic data, which hasfour data units: C, G, A, and T. Those four data units can berepresented as 2 bit sequences: 00, 01, 10, and 11, which can beprocessed and stored using the method described herein.

It will be recognized by a person skilled in the art that certainembodiments of the methods described herein may have uses other thandata storage. For example, because the data is stored in reference codeform, it cannot be reconstructed without the availability of the libraryof sourceblocks. This is effectively a form of encryption, which couldbe used for cyber security purposes. As another example, an embodimentof the method described herein could be used to store backup copies ofdata, provide for redundancy in the event of server failure, or provideadditional security against cyberattacks by distributing multiplepartial copies of the library among computers are various locations,ensuring that at least two copies of each sourceblock exist in differentlocations within the network.

FIG. 18 is a flow diagram illustrating the use of a data encoding systemused to recursively encode data to further reduce data size. Data may beinput 1805 into a data deconstruction engine 102 to be deconstructedinto code references, using a library of code references based on theinput 1810. Such example data is shown in a converted, encoded format1815, highly compressed, reducing the example data from 96 bits of data,to 12 bits of data, before sending this newly encoded data through theprocess again 1820, to be encoded by a second library 1825, reducing iteven further. The newly converted data 1830 is shown as only 6 bits inthis example, thus a size of 6.25% of the original data packet. Withrecursive encoding, then, it is possible and implemented in the systemto achieve increasing compression ratios, using multi-layered encoding,through recursively encoding data. Both initial encoding libraries 1810and subsequent libraries 1825 may be achieved through machine learningtechniques to find optimal encoding patterns to reduce size, with thelibraries being distributed to recipients prior to transfer of theactual encoded data, such that only the compressed data 1830 must betransferred or stored, allowing for smaller data footprints andbandwidth requirements. This process can be reversed to reconstruct thedata. While this example shows only two levels of encoding, recursiveencoding may be repeated any number of times. The number of levels ofrecursive encoding will depend on many factors, a non-exhaustive list ofwhich includes the type of data being encoded, the size of the originaldata, the intended usage of the data, the number of instances of databeing stored, and available storage space for codebooks and libraries.Additionally, recursive encoding can be applied not only to data to bestored or transmitted, but also to the codebooks and/or libraries,themselves. For example, many installations of different libraries couldtake up a substantial amount of storage space. Recursively encodingthose different libraries to a single, universal library woulddramatically reduce the amount of storage space required, and eachdifferent library could be reconstructed as necessary to reconstructincoming streams of data.

FIG. 20 is a flow diagram of an exemplary method used to detectanomalies in received encoded data and producing a warning. A system mayhave trained encoding libraries 2010, before data is received from somesource such as a network connected device or a locally connected deviceincluding USB connected devices, to be decoded 2020. Decoding in thiscontext refers to the process of using the encoding libraries to takethe received data and attempt to use encoded references to decode thedata into its original source 2030, potentially more than once ifrecursive encoding was used, but not necessarily more than once. Ananomaly detector 1910 may be configured to detect a large amount ofun-encoded data 2040 in the midst of encoded data, by locating data orreferences that do not appear in the encoding libraries, indicating atleast an anomaly, and potentially data tampering or faulty encodinglibraries. A flag or warning is set by the system 2050, allowing a userto be warned at least of the presence of the anomaly and thecharacteristics of the anomaly. However, if a large amount of invalidreferences or unencoded data are not present in the encoded data that isattempting to be decoded, the data may be decoded and output as normal2060, indicating no anomaly has been detected.

FIG. 21 is a flow diagram of a method used for Distributed Denial ofService (DDoS) attack denial. A system may have trained encodinglibraries 2110, before data is received from some source such as anetwork connected device or a locally connected device including USBconnected devices, to be decoded 2120. Decoding in this context refersto the process of using the encoding libraries to take the received dataand attempt to use encoded references to decode the data into itsoriginal source 2130, potentially more than once if recursive encodingwas used, but not necessarily more than once. A DDoS detector 1920 maybe configured to detect a large amount of repeating data 2140 in theencoded data, by locating data or references that repeat many times over(the number of which can be configured by a user or administrator asneed be), indicating a possible DDoS attack. A flag or warning is set bythe system 2150, allowing a user to be warned at least of the presenceof a possible DDoS attack, including characteristics about the data andsource that initiated the flag, allowing a user to then block incomingdata from that source. However, if a large amount of repeat data in ashort span of time is not detected, the data may be decoded and outputas normal 2160, indicating no DDoS attack has been detected.

FIG. 23 is a flow diagram of an exemplary method used to enablehigh-speed data mining of repetitive data. A system may have trainedencoding libraries 2310, before data is received from some source suchas a network connected device or a locally connected device includingUSB connected devices, to be analyzed 2320 and decoded 2330. Whendetermining data for analysis, users may select specific data todesignate for decoding 2330, before running any data mining or analyticsfunctions or software on the decoded data 2340. Rather than havingtraditional decryption and decompression operate over distributeddrives, data can be regenerated immediately using the encoding librariesdisclosed herein, as it is being searched. Using methods described inFIG. 9 and FIG. 11 , data can be stored, retrieved, and decoded swiftlyfor searching, even across multiple devices, because the encodinglibrary may be on each device. For example, if a group of servers hostcodewords relevant for data mining purposes, a single computer canrequest these codewords, and the codewords can be sent to the recipientswiftly over the bandwidth of their connection, allowing the recipientto locally decode the data for immediate evaluation and searching,rather than running slow, traditional decompression algorithms on datastored across multiple devices or transfer larger sums of data acrosslimited bandwidth.

FIG. 25 is a flow diagram of an exemplary method used to encode andtransfer software and firmware updates to a device for installation, forthe purposes of reduced bandwidth consumption. A first system may havetrained code libraries or “codebooks” present 2510, allowing for asoftware update of some manner to be encoded 2520. Such a softwareupdate may be a firmware update, operating system update, securitypatch, application patch or upgrade, or any other type of softwareupdate, patch, modification, or upgrade, affecting any computer system.A codebook for the patch must be distributed to a recipient 2530, whichmay be done beforehand and either over a network or through a local orphysical connection, but must be accomplished at some point in theprocess before the update may be installed on the recipient device 2560.An update may then be distributed to a recipient device 2540, allowing arecipient with a codebook distributed to them 2530 to decode the update2550 before installation 2560. In this way, an encoded and thus heavilycompressed update may be sent to a recipient far quicker and with lessbandwidth usage than traditional lossless compression methods for data,or when sending data in uncompressed formats. This especially maybenefit large distributions of software and software updates, as withenterprises updating large numbers of devices at once.

FIG. 27 is a flow diagram of an exemplary method used to encode newsoftware and operating system installations for reduced bandwidthrequired for transference. A first system may have trained codelibraries or “codebooks” present 2710, allowing for a softwareinstallation of some manner to be encoded 2720. Such a softwareinstallation may be a software update, operating system, securitysystem, application, or any other type of software installation,execution, or acquisition, affecting a computer system. An encodinglibrary or “codebook” for the installation must be distributed to arecipient 2730, which may be done beforehand and either over a networkor through a local or physical connection but must be accomplished atsome point in the process before the installation can begin on therecipient device 2760. An installation may then be distributed to arecipient device 2740, allowing a recipient with a codebook distributedto them 2730 to decode the installation 2750 before executing theinstallation 2760. In this way, an encoded and thus heavily compressedsoftware installation may be sent to a recipient far quicker and withless bandwidth usage than traditional lossless compression methods fordata, or when sending data in uncompressed formats. This especially maybenefit large distributions of software and software updates, as withenterprises updating large numbers of devices at once.

FIG. 31 is a method diagram illustrating the steps 3100 involved inusing an embodiment of the codebook training system to update acodebook. The process begins when requested data is received 3101 by acodebook training module. The requested data may comprise a plurality ofsourceblocks. Next, the received data may be stored in a cache andformatted into a test dataset 3102. The next step is to retrieve thepreviously computed probability distribution associated with theprevious (most recent) training dataset from a storage device 3103.Using one or more algorithms, measure and record the probabilitydistribution of the test dataset 3104. The step after that is to comparethe measured probability distributions of the test dataset and theprevious training dataset to compute the difference in distributionstatistics between the two datasets 3105. If the test datasetprobability distribution exceeds a pre-determined difference threshold,then the test dataset will be used to retrain the encoding/decodingalgorithms 3106 to reflect the new distribution of the incoming data tothe encoder/decoder system. The retrained algorithms may then be used tocreate new data sourceblocks 3107 that better capture the nature of thedata being received. These newly created data sourceblocks may then beused to create new codewords and update a codebook 3108 with each newdata sourceblock and its associated new codeword. Last, the updatedcodebooks may be sent to encoding and decoding machines 3109 in order toensure the encoding/decoding system function properly.

FIG. 37 is a diagram illustrating an exemplary method for codebookgeneration from a training data set. In this simplified example, atraining data set 3700 comprised of nine different potential types ofsourceblocks 3701 is received. The number of sourceblocks actuallyreceived is 10, one of sourceblock A, four of sourceblock C, two ofsourceblock E, and three of sourceblock H. This is a low-entropy dataset in that the types of sourceblocks actually received are highlyregular (lots of A, C, E, and H, and none of B, D, F, G, and I). None ofthe other types of sourceblocks are received. The frequency ofoccurrence of each sourceblock, therefore, is 10% A, 40% C, 20% E, and30% H. Using these frequencies of occurrence, a codebook could becreated with no mismatch codeword as shown in the codebook 3710represented by a Huffman binary tree having three empty nodes, x 3710 x,y 3710 y, and z 3710 z, leading to leaf nodes for sourceblocks C 3710 c,H 3710 h, E 3710 e, and A 3710 a, according to their frequencies ofoccurrence in training data set 3700. This codebook 3710 representscodewords for sourceblocks C, H, E, and A as follows: C→0, H→10, E→110,and A→111 by following the appropriate paths of the codebook 3710.However, this codebook does not account for the relatively highprobability of occurrence of mismatches during encoding if sourceblocksB, D, F, G, and I appear in the data to be encoded. If this codebook3710 is used, sourceblocks B, D, F, G, and I could not be encoded. If aHuffman binary tree was created to include sourceblocks B, D, F, G, andI, each of them would be assigned increasingly inefficient leaf nodesafter the lowest probability leaf node in the tree, A 3710 a. Forexample, if B was assigned after A 3701 a, its codeword would be 1110,followed by D with a codeword of 11110, and so on.

To address this problem of inability to assign codewords or inefficiencyin assigning codewords using a low-entropy training data set, a codebook3720 can be created with a mismatch codeword MIS 3710 m insertedrepresenting the probability of mismatch during encoding. If themismatch probability estimate 3704 is 30% (equivalent in probability toreceiving sourceblock H), for example, the resulting codebook 3720 wouldinclude an additional empty node q 3710 q leading to leaf node MIS 3710m, at a roughly equivalent level of probability (and corresponding shortcodeword) as sourceblock C 3710 c and sourceblock H 3710 h. Thiscodebook 3720 represents codewords for sourceblocks C, MIS, H, E, and Aas follows: C→00, MIS→01, H→10, E→110, and A→111 by following theappropriate paths of the codebook 3720. Unlike codebook 3710, however,codebook 3720 is capable of coding for any arbitrary mismatchsourceblock received, including but not limited to sourceblocks B, D, F,G, and I. During encoding, a codework result of 01 (MIS) triggers asecondary encoding method for the mismatched sourceblock. A variety ofsecondary encoding methods may be used including, but not limited to noencoding (i.e., using the sourceblock as received) or using a suboptimalbut guaranteed-to-work entropy encoding method that uses a shorterblock-length for encoding.

While this example uses a single mismatch codeword, in otherembodiments, multiple mismatch codewords may be used, signaling, forexample, different probabilities of mismatches for different types ofsourceblocks. Further, while this example uses a single secondaryencoding method, other embodiments may use a plurality of such secondarymethods, or additional levels of encoding methods (tertiary, quaternary,etc.). Multiple mismatch codewords may be associated with the pluralityof secondary methods and/or additional levels of encoding methods.

Decoding of data compacted using this method is the reverse of theencoding process. A stream of codewords are received. Any codewords fromthe codebook (the primary encoding) are looked up in the codebook toretrieve their associated sourceblocks. Any codewords from secondaryencoding are looked up using the secondary encoding method to retrievetheir associated sourceblocks.

FIG. 38 is a diagram illustrating an exemplary encoding using a codebookand secondary encoding. In this example, the sourceblocks A-I 3701,codebook 3720, and codewords 3721 are the same as in FIG. 37 . Codebook3720 contains the same codewords 3721 The data to be encoded 3810consists of a stream of sourceblocks in the order from first to last:AEABCCHHCC. The first three sourceblocks processed are AEA, and areencoded as 111, 110, and 111 using codebook 3720 as the primary encodingmethod as shown at 3820. However, the fourth sourceblock 3811 is B,which is not contained in codebook 3720. Thus, when B is processed,codebook 3720 returns the mismatch code 01 3821, which triggers asecondary encoding method 3820 for encoding sourceblock B. In thisexample, secondary encoding method 3850 is a suboptimal 4-bit encodingof sourceblocks A-I 3701, wherein the codewords for B, D, F, G, and Iare as follows: B→0010, D→0100, F→0110, G→0111, and I→1000. Thesecondary encoding 3820 of B is 0010, which is inserted into the encodeddata stream 3831. From there, encoding reverts to the primary encodingmethod using codebook 3720 as shown at 3840, and the remainder of thesourceblocks are encoded according to codebook 3720. Note that while thesecondary encoding is shown as being performed while primary encoding isoccurring, other embodiments may allow primary encoding to completebefore performing secondary encoding, and may even allow the primaryencoding with the mismatched codewords to be stored such that thesecondary encoding is performed at a later time, although suchembodiments would need some record of the association between themismatch codeword and the sourceblock that it replaced (which could bedone by several means including, but not limited to, reprocessing thedata to be encoded, storing a separate record of the associations, andusing multiple mismatch codewords.

Decoding of data compacted using this method is the reverse of theencoding process. A stream of codewords are received. Any codewords fromthe codebook (the primary encoding) are looked up in the codebook toretrieve their associated sourceblocks. Any codewords from secondaryencoding are looked up using the secondary encoding method to retrievetheir associated sourceblocks.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 39 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™ THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 39 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 40 , there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20 and may be useful for providing common servicesto client applications 24. Services 23 may for example be WINDOWS™services, user-space common services in a Linux environment, or anyother type of common service architecture used with operating system 21.Input devices 28 may be of any type suitable for receiving user input,including for example a keyboard, touchscreen, microphone (for example,for voice input), mouse, touchpad, trackball, or any combinationthereof. Output devices 27 may be of any type suitable for providingoutput to one or more users, whether remote or local to system 20, andmay include for example one or more screens for visual output, speakers,printers, or any combination thereof. Memory 25 may be random-accessmemory having any structure and architecture known in the art, for useby processors 21, for example to run software. Storage devices 26 may beany magnetic, optical, mechanical, memristor, or electrical storagedevice for storage of data in digital form (such as those describedabove, referring to FIG. 39 ). Examples of storage devices 26 includeflash memory, magnetic hard drive, CD-ROM, and/or the like.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 41 , there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 40 . In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as Wi-Fi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 may be used or referred to by one or more aspects. Itshould be understood by one having ordinary skill in the art thatdatabases 34 may be arranged in a wide variety of architectures andusing a wide variety of data access and manipulation means. For example,in various aspects one or more databases 34 may comprise a relationaldatabase system using a structured query language (SQL), while othersmay comprise an alternative data storage technology such as thosereferred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™,GOOGLE BIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database,” it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 42 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for encoding data using mismatchprobability estimation, comprising: a computing device comprising aprocessor, a memory, and a non-volatile data storage device; astatistical analyzer comprising a first plurality of programminginstructions stored in the memory which, when operating on theprocessor, causes the computing device to: calculate a mismatchprobability estimate comprising a probability that any given sourceblockin a non-training data set to be later received for encoding will not bea sourceblock that was contained in a training data set; generate amismatch sourceblock representing sourceblocks that were not containedin the training data set, and assign the mismatch probability estimateto the mismatch sourceblock as the frequency of occurrence of themismatch sourceblock; and a codebook generator comprising a secondplurality of programming instructions stored in the memory which, whenoperating on the processor, causes the computing device to: generate acodebook from the sourceblocks of the training data set and the mismatchsourceblock using an entropy encoding method wherein codewords areassigned to each sourceblock based on its frequency of occurrence. 2.The system of claim 1, further comprising an encoder comprising a thirdplurality of programming instructions stored in the memory which, whenoperating on the processor, causes the computing device to: receive thenon-training data set for encoding, the non-training data set comprisingsourceblocks of data; for each sourceblock of the non-training data set,look up and return the codeword for that sourceblock in the codebook andinsert that codeword into an encoded data stream; where the returnedcodeword is the codeword for the mismatch sourceblock, generate a newcodeword for the looked up sourceblock using a secondary encodingmethod, and insert the new codeword into the encoded data stream.
 3. Thesystem of claim 1, further comprising a decoder comprising a fourthplurality of programming instructions stored in the memory which, whenoperating on the processor, causes the computing device to: receive anencoded data stream comprising codewords; for each codeword in theencoded data stream, look up and return the sourceblock for thatcodeword in the codebook and insert that sourceblock into a decoded datastream; and where the returned sourceblock is the mismatch sourceblock,determine the sourceblock for that codeword using the secondary encodingmethod, and insert the determined sourceblock into the decoded datastream.
 4. The system of claim 1, wherein the training data set is alow-entropy data set, either having a small subset of sourceblocks of agiven size relative to the total possible number of sourceblocks of thatsize or having a set of sourceblocks closely matching the set ofsourceblocks expected in the non-training data set.
 5. The system ofclaim 1, wherein the entropy encoding method is Huffman coding or aknown variant thereof.
 6. The system of claim 1, wherein the mismatchprobability estimate, q, is calculated as q=M/N, where: M is the numberof times a previously-unobserved sourceblock appeared in the trainingdata set; and N is the total number of sourceblocks observed in thetraining data set.
 7. The system of claim 6, wherein the mismatchprobability estimate, q, is calculated as q=M/N=(Σ_(j=1) ^(N)X_(j))/N,where: $X_{j} = \left\{ {\begin{matrix}{{1{if}S_{j}} \notin \left\{ {{S_{i}:1} \leq i < j} \right\}} \\{0{otherwise}}\end{matrix};} \right.$ and N is the total number of sourceblocksobserved in the training data set.
 8. The system of claim 7, wherein anexponentially-weighted moving average is applied to the calculation ofq=(Σ_(j=1) ^(N)X_(j))/N.
 9. The system of claim 8, wherein theexponentially-weighted moving average is a modified form of anexponentially-weighted moving average of the form:$\mu_{j} = \left\{ {\begin{matrix}{{1{if}j} = 0} \\{{{\left( {1 - \beta_{j}} \right)\mu_{j - 1}} + {\beta_{j}X_{j}{if}j}} > 0}\end{matrix};} \right.$whereβ_(j) = Clog (j)/jandβ₁ = 1, forsomeconstantC.
 10. A method forencoding data using mismatch probability estimation, comprising thesteps of: using a statistical analyzer operating on a computing devicecomprising a memory and a processor to: calculate a mismatch probabilityestimate comprising a probability that any given sourceblock in anon-training data set to be later received for encoding will not be asourceblock that was contained in a training data set; generate amismatch sourceblock representing sourceblocks that were not containedin the training data set, and assign the mismatch probability estimateto the mismatch sourceblock as the frequency of occurrence of themismatch sourceblock; and using a codebook generator operating on thecomputing device to: generate a codebook from the sourceblocks of thetraining data set and the mismatch sourceblock using an entropy encodingmethod wherein codewords are assigned to each sourceblock based on itsfrequency of occurrence.
 11. The method of claim 10, further comprisingthe step of using an encoder operating on the computing device to:receive the non-training data set for encoding, the non-training dataset comprising sourceblocks of data; for each sourceblock of thenon-training data set, look up and return the codeword for thatsourceblock in the codebook and insert that codeword into an encodeddata stream; where the returned codeword is the codeword for themismatch sourceblock, generate a new codeword for the looked upsourceblock using a secondary encoding method, and insert the newcodeword into the encoded data stream.
 12. The method of claim 10,further comprising the step of using a decoder operating on thecomputing device to: receive an encoded data stream comprisingcodewords; for each codeword in the encoded data stream, look up andreturn the sourceblock for that codeword in the codebook and insert thatsourceblock into a decoded data stream; and where the returnedsourceblock is the mismatch sourceblock, determine the sourceblock forthat codeword using the secondary encoding method, and insert thedetermined sourceblock into the decoded data stream.
 13. The method ofclaim 10, wherein the training data set is a low-entropy data set,either having a small subset of sourceblocks of a given size relative tothe total possible number of sourceblocks of that size or having a setof sourceblocks closely matching the set of sourceblocks expected in thenon-training data set.
 14. The method of claim 10, wherein the entropyencoding method is Huffman coding or a known variant thereof.
 15. Themethod of claim 10, wherein the mismatch probability estimate, q, iscalculated as q=M/N, where: M is the number of times apreviously-unobserved sourceblock appeared in the training data set; andN is the total number of sourceblocks observed in the training data set.16. The method of claim 15, wherein the mismatch probability estimate,q, is calculated as q=M/N=(Σ_(j=1) ^(N)X_(j))/N, where:$X_{j} = \left\{ {\begin{matrix}{{1{if}S_{j}} \notin \left\{ {{S_{i}:1} \leq i < j} \right\}} \\{0{otherwise}}\end{matrix};{and}} \right.$ N is the total number of sourceblocksobserved in the training data set.
 17. The method of claim 16, whereinan exponentially-weighted moving average is applied to the calculationof q=(Σ_(j=1) ^(N)X_(j))/N.
 18. The method of claim 17, wherein theexponentially-weighted moving average is a modified form of anexponentially-weighted moving average of the form:$\mu_{j} = \left\{ {\begin{matrix}{{1{if}j} = 0} \\{{{\left( {1 - \beta_{j}} \right)\mu_{j - 1}} + {\beta_{j}X_{j}{if}j}} > 0}\end{matrix};} \right.$whereβ_(j) = Clog (j)/jandβ₁ = 1, forsomeconstantC.